Transformers

Hugging Face Transformers is the standard Python library for working with pre-trained AI models: text generation, translation, summarization, image classification, speech, and more. The Hub it connects to lists over a million Transformers checkpoints. Load a model in three lines of code, run inference, done. Apache 2.0 and free, with no gated features.

One change matters for anyone following older tutorials. Version 5 removed the TensorFlow and JAX backends, so the library is PyTorch only and needs Python 3.10 or later. The pipeline API still takes you from zero to a working sentiment classifier in one line, and the Trainer API handles the training loop, checkpointing, and evaluation for fine-tuning.

The library is free. The Hugging Face Hub is free too, with 100 GB of private storage. Pro is $9/mo for 1 TB. Team is $20 per user per month and adds SSO and audit logs, and Enterprise is $50 per user. Solo and small teams: free library, free Hub. Medium: Team once you need SSO. Large: Enterprise, or your own GPUs.

The catch: big models need serious GPU hardware. A 7B parameter model needs about 14 GB of VRAM just to load in half precision. Managed Inference Endpoints start at $0.03/hr on CPU, with a T4 GPU at $0.50/hr and an A100 at $2.50/hr. The install is heavy because it pulls in PyTorch. For production serving, vllm-project/vllm or ggml-org/llama.cpp will perform better.